Obesometric factors associated with increased skin-to-stone distances in renal stone patients.
Bibliographic record
Abstract
INTRODUCTION: Obese patients are at increased risk for renal stones as well as treatment failures due to increased skin-to-stone distances (SSD) and harder stone compositions. We investigated the relationships between obesometric parameters (body mass index [BMI], body fat distribution and obesity-related hormone levels) with SSD and stone hardness. MATERIALS AND METHODS: We prospectively enrolled patients undergoing stone interventions at our institution. Computed tomography (CT) scans were analyzed; adipose tissue was identified according to Hounsfield units (HU) and separated into subcutaneous (SAT) and visceral (VAT) components. The pixels were averaged at three levels to calculate fat distribution: %VAT = (VAT)/(VAT + SAT). SSD was measured and HU were used as a surrogate for stone hardness. Obesity-related hormones leptin and adiponectin were measured by ELISA. RESULTS: Seventy-nine patients were prospectively enrolled. Mean BMI and %VAT were 30.02 kg/m2 and 40.13 kg/m2. Mean leptin and adiponectin levels were 17.5 ng/mL and 7.67 mcg/mL indicating high risk for metabolic consequences of obesity. Females had greater proportions of subcutaneous fat than males (%VAT 28.4 versus 46.94, p < 0.001) and greater SSD (11.26 cm versus 9.86 cm, p = 0.025). Among obese patients, subcutaneous fat correlated with SSD independently of BMI (r = 0.454, p = 0.008). Obese patients with %VAT > 40 versus < 40 had SSD of 11.35 cm versus 13.7 cm (p = 0.005). Diabetics had harder stone compositions as measured by HU than non-diabetics (982.86 versus 648.86, p = 0.001). CONCLUSION: Obesometric parameters such as BMI, body fat distribution, and the presence of diabetes mellitus are important considerations in the management of renal stone disease. A large proportion of subcutaneous fat, which can be estimated by physical examination, predicts SSD among obese patients and may aid treatment decisions in patients, particularly those without pre-treatment CT scans. Further studies are needed to refine the role of obesometrics in personalizing treatment decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".